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deepkoopman

参考论文A Data-Efficient Reinforcement Learning Method Based on Local Koopman Operators的koopman方案实现

1. 环境

需要pytorch,numpy

2. 运行与结果

运行python main.py即可,可以修改env 会间隔输出拟合结果对比,最终输出一个test_error的图

3. 代码结构

  • main.py:主程序,包括生成初始轨迹,调用训练,与训练结果测试
  • deep_util.py:训练程序包括网络定义,replaybuffer定义,训练定义
  • 'nonlinear_system.py':非线性系统的定义,含有VanDerPol,弹簧阻尼非线性系统,和强非线性的奖励
  • 'test_all.py':用pytest的单元测试

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